Learning people movement model from multiple cameras for behaviour recognition Nguyen, Nam T., Venkatesh, Svetha, West, Geoff A. W. and Bui, Hung H. 2004, Learning people movement model from multiple cameras for behaviour recognition. In Fred, Ana, Caelli, Terry, Duin, Robert P.W., Campilho, Aurélio and de Ridder, Dick (ed), Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings, Springer-Verlag, Berlin, Germany, pp.315-324, doi: 10.1007/978-3-540-27868-9_33. Attached Files Name Description MIMEType Size Downloads Title Learning people movement model from multiple cameras for behaviour recognition Author(s) Nguyen, Nam T. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 West, Geoff A. W. Bui, Hung H. Title of book Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings Editor(s) Fred, Ana Caelli, Terry Duin, Robert P.W. Campilho, Aurélio de Ridder, Dick Publication date 2004 Series Lecture notes in computer science ; 3138 Chapter number 33 Total chapters 127 Start page 315 End page 324 Total pages 10 Publisher Springer-Verlag Place of Publication Berlin, Germany Keyword(s) behaviour prediction Abstract Hidden Markov mEmory Model Summary In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures. ISBN 9783540225706 3540225706 ISSN 0302-9743 Language eng DOI 10.1007/978-3-540-27868-9_33 Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1.1 Book chapter Copyright notice ©2004, Springer-Verlag Berlin Heidelberg Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044663 Document type: Book Chapter Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 20 Apr 2012, 13:24:00 EST Mon, 23 Apr 2012, 12:47:19 EST Mon, 23 Apr 2012, 12:49:53 EST Wed, 02 May 2012, 08:37:05 EST Thu, 20 Nov 2014, 11:03:12 EST Thu, 20 Nov 2014, 11:03:54 EST Sat, 21 Oct 2017, 02:01:35 EST Fri, 08 Nov 2019, 06:37:54 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 727 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 13:24:00 EST

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